In this paper, we present a novel approach for fusing shape and texture local binary patterns (LBP) for 3D face recognition. Using the framework proposed in [1], we compute LBP directly on the face mesh surface, then we construct a grid of the regions on the facial surface that can accommodate global and partial descriptions. Compared to its depth-image counterpart, our approach is distinguished by the following features: a) inherits the intrinsic advantages of mesh surface; b) does not require normalization; c) can accommodate partial matching. In addition, it allows early-level fusion of texture and shape modalities. Through experiments conducted on the BU-3DFE and Bosphorus databases, we assess different variants of our approach with regard to facial expressions and missing data.